
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__  :     Tuple   				=       argparse.ArgumentParser(
					    description=(
					        "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
					        " Distillation"
					    )
					)
					parser.add_argument("--model_type", default="bert", choices=["bert"])
					parser.add_argument("--model_name", default="bert-base-uncased", type=str)
					parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
					parser.add_argument("--vocab_transform", action="store_true")
					SCREAMING_SNAKE_CASE__  :     Tuple   				=       parser.parse_args()

					if args.model_type == "bert":
										SCREAMING_SNAKE_CASE__  :     List[Any]   				=       BertForMaskedLM.from_pretrained(args.model_name)
										SCREAMING_SNAKE_CASE__  :     Any   				=       "bert"
					else:
										raise ValueError("args.model_type should be \"bert\".")

					SCREAMING_SNAKE_CASE__  :     Union[str, Any]   				=       model.state_dict()
					SCREAMING_SNAKE_CASE__  :     str   				=       {}

					for w in ["word_embeddings", "position_embeddings"]:
										SCREAMING_SNAKE_CASE__  :     int   				=       state_dict[F'{prefix}.embeddings.{w}.weight']
					for w in ["weight", "bias"]:
										SCREAMING_SNAKE_CASE__  :     Any   				=       state_dict[F'{prefix}.embeddings.LayerNorm.{w}']

					SCREAMING_SNAKE_CASE__  :     int   				=       0
					for teacher_idx in [0, 2, 4, 7, 9, 11]:
										for w in ["weight", "bias"]:
															SCREAMING_SNAKE_CASE__  :     int   				=       state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
															]
															SCREAMING_SNAKE_CASE__  :     Dict   				=       state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
															]
															SCREAMING_SNAKE_CASE__  :     Optional[int]   				=       state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
															]

															SCREAMING_SNAKE_CASE__  :     int   				=       state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
															]
															SCREAMING_SNAKE_CASE__  :     Tuple   				=       state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
															]

															SCREAMING_SNAKE_CASE__  :     Any   				=       state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
															]
															SCREAMING_SNAKE_CASE__  :     Optional[Any]   				=       state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
															]
															SCREAMING_SNAKE_CASE__  :     Union[str, Any]   				=       state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
															]
										std_idx += 1

					SCREAMING_SNAKE_CASE__  :     int   				=       state_dict["cls.predictions.decoder.weight"]
					SCREAMING_SNAKE_CASE__  :     Union[str, Any]   				=       state_dict["cls.predictions.bias"]
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															SCREAMING_SNAKE_CASE__  :     Union[str, Any]   				=       state_dict[F'cls.predictions.transform.dense.{w}']
															SCREAMING_SNAKE_CASE__  :     List[str]   				=       state_dict[F'cls.predictions.transform.LayerNorm.{w}']

					print(F'N layers selected for distillation: {std_idx}')
					print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

					print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
					torch.save(compressed_sd, args.dump_checkpoint)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


